{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T21:29:11Z","timestamp":1788038951066,"version":"build-2784847793"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002496","name":"Hongik University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002496","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Rheumatoid arthritis (RA) is a chronic inflammatory disease with considerable heterogeneity in treatment response, leaving many patients unable to achieve remission or low disease activity. We aimed to develop a machine learning model to predict which patients with moderate-to-severe RA would respond to Janus kinase inhibitor therapy, thereby facilitating more effective and personalized treatment strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively collected data from the Korean College of Rheumatology Biologics therapy (KOBIO) registry and Asan Medical Centers, including adult patients with moderate or high disease activity (DAS28-ESR\u22653.2) and at least 12 months of follow-up. We trained and validated gradient boosting machine-learning models (XGBoost) to predict whether patients would achieve low disease activity or remission after 6 months of Janus kinase inhibitor therapy, using prespecified baseline covariates and stratified splits for independent training and test datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This study included 264 patients with moderate-to-severe rheumatoid arthritis from the Korean cohorts (the KOBIO registry and Asan Medical Centers). Of these, 247 received either tofacitinib (n=123) or baricitinib (n=124). After 6 months of treatment, 65% of patients on tofacitinib and 70% on baricitinib achieved low disease activity or remission. Our machine-learning models (trained and validated separately for each drug) achieved high predictive performance (tofacitinib: ROC-AUC 0.82, accuracy 80%; baricitinib: ROC-AUC 0\u00b788, accuracy 88%), identifying key clinical factors such as total cholesterol, CRP, and specific joint swelling or tenderness for tofacitinib, and patient global assessment, joint swelling, and co-administration of hydroxychloroquine for baricitinib. Model-guided treatment selection could have improved outcomes for an additional 15% of patients by aligning each individual\u2019s predicted response with the more suitable Janus kinase inhibitor.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The findings suggest that ML models can accurately predict treatment response to Janus kinase inhibitors in rheumatoid arthritis and may support personalized therapy selection to improve clinical outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1689144","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T06:34:00Z","timestamp":1764138840000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine learning-based prediction of response to Janus kinase inhibitors in patients with rheumatoid arthritis using clinical data"],"prefix":"10.3389","volume":"16","author":[{"given":"Yeo-Jin","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gyucheol","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joongyeub","family":"Yeo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyeong","family":"Baek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heeju","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minji","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong-Gil","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo 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